π€ AI Summary
This work addresses the limitations of traditional audio effect interfaces, which rely on discrete parameters and hinder holistic perception and fluid exploration of sound transformation spaces. The authors propose a perceptually grounded two-dimensional map interface that organizes audio effects into a continuous soundscape, integrating spatial interaction, DAW-style controls, and embedded machine learning. This system uniquely combines interpretable DAW-style parameter control with embedding-based semantic search, enabling continuous browsing, interpolation, and fine-grained editing of audio effects within a unified interface. Experimental results demonstrate that the approach significantly enhances usersβ intuitive understanding of and fluency in navigating sound transformation spaces during music creation, production, and performance.
π Abstract
Audio effects (FX) shape sound in contemporary music practice. However, most interfaces present them as discrete modules and parameters that favor targeted adjustment over exploratory listening. This separation can make it difficult to build intuition about the broader space of possible transformations or to move fluidly between searching and refinement. We present FXplorer, an interface that organizes audio effects within a perceptually informed 2D space, allowing sound transformations to be browsed as a continuous landscape rather than as isolated presets. By combining established spatial interaction approaches and interpretable DAW-style controls with recent embedding-based machine learning methods for similarity and semantic search, the system brings exploration and parameter refinement into a single workspace. FXplorer supports composition, production, or performance by allowing users to edit and interpolate between effect presets interactively.